A Revenue Operations case study using 1,250 synthetic B2B SaaS leads to investigate funnel performance, revenue efficiency, customer segmentation, and the company's ideal customer profile.
Elix.ai generated 1,250 leads across several industries, regions, company sizes, and acquisition channels. The objective was to determine where the funnel was losing value and which customers deserved greater sales and marketing investment.
Rather than evaluating success through lead volume alone, the analysis combined conversion performance with won ARR and ARR per lead.
Which customers create the greatest commercial value for Elix.ai, and how should that change its go-to-market strategy?
The analysis began with the overall funnel and progressively segmented performance by lead source, company size, industry, and region. Strong characteristics were then combined into an ICP hypothesis and tested against the remainder of the customer base.
Measured progression from lead through qualification, demo, opportunity, and closed won.
Added ARR per lead to distinguish high-volume channels from genuinely valuable acquisition.
Compared company size, industry, and geography to identify recurring performance patterns.
Combined the strongest characteristics and tested the resulting segment across the full funnel.
Several segments appeared healthy when viewed only through conversion. Adding ARR per lead changed the picture by showing how much commercial value each acquired lead ultimately produced.
Larger companies generated dramatically greater revenue efficiency, with 1000+ employee accounts producing the strongest ARR per lead.
SaaS and Fintech produced the strongest combination of qualification, conversion, and revenue efficiency.
DACH led regional performance, followed by the Nordics and Benelux.
Despite reasonable deal value, Healthcare required substantial funnel effort while producing weak overall revenue efficiency.
The individual characteristics looked promising, but that did not guarantee that their combination would outperform the wider business. The next step was therefore to test the proposed ICP as a single segment.
| Segment | Leads | Wins | Win rate | Avg deal | ARR / lead |
|---|---|---|---|---|---|
| Target ICP | 50 | 10 | 20.0% | €68.4k | €13,680 |
| Other | 1,200 | 95 | 7.9% | €33.6k | €2,663 |
Target ICP leads generated more than 5× the ARR per lead of the rest of the database while also converting at more than twice the overall rate.
Target ICP accounts outperformed other leads at every measured stage of the funnel.
| Funnel stage | Target ICP | Other |
|---|---|---|
| Lead → Qualified | 70.0% | 50.3% |
| Qualified → Demo | 68.6% | 56.8% |
| Demo → Opportunity | 66.7% | 57.7% |
| Opportunity → Win | 62.5% | 48.0% |
Once acquisition was restricted to the target ICP, most channels generated strong commercial outcomes.
| Lead source | ICP leads | Win rate | ARR / lead |
|---|---|---|---|
| Referral | 6 | 50.0% | €38,250 |
| Partners | 7 | 28.6% | €24,500 |
| Organic Search | 7 | 28.6% | €17,000 |
| Paid Search | 6 | 16.7% | €10,667 |
| Outbound | 10 | 20.0% | €10,000 |
| 8 | 0% | €0 | |
| Events | 6 | 0% | €0 |
Referral, Partners, and Organic Search showed the strongest revenue efficiency. Outbound also performed well when directed toward the ICP, suggesting that its earlier weakness was partly a targeting problem rather than simply a channel problem.
This project uses a synthetic dataset created for analytical practice and portfolio demonstration. Some segmented samples are small, meaning extreme conversion rates should be interpreted directionally.
The analysis identifies associations and commercial patterns rather than proving causation. The resulting ICP should therefore be treated as a hypothesis to test through future acquisition and sales performance.